Abstract
The growing processing capabilities of mobile devices coupled with portable and wearable sensors have enabled the development of context-aware services tailored to the user environment and its daily activities. The problem of determining the user context at each particular point in time is one of the main challenges in this area. In this paper, we describe the approach pursued in the UPCASE project, which makes use of sensors available in the mobile device as well as sensors externally connected via Bluetooth. We describe the system architecture from raw data acquisition to feature extraction and context inference. As a proof of concept, the inference of contexts is based on a decision tree to learn and identify contexts automatically and dynamically at runtime. Preliminary results suggest that this is a promising approach for context inference in several application scenarios.
This work was partially funded by PT Inovação S.A.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-642-01802-2_30
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Santos, A.C., Tarrataca, L., Cardoso, J.M.P., Ferreira, D.R., Diniz, P.C., Chainho, P. (2009). Context Inference for Mobile Applications in the UPCASE Project. In: Bonnin, JM., Giannelli, C., Magedanz, T. (eds) MobileWireless Middleware, Operating Systems, and Applications. MOBILWARE 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 7. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01802-2_26
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DOI: https://doi.org/10.1007/978-3-642-01802-2_26
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